Introduction to R
Jan Vandepitte
30 August 2018
0. What is R
- Open source Script language for statistical programming
- data preparation, machine learning, experimentation, visualization (presentation, notebook, shiny dashboard)
- packaging system (CRAN) like nuget, npm
x <- 1
x + 1
[1] 2
0.1 Origins in LISP
LISP -> Scheme -> S -> R (cfr Ecmascript)
- REPL
- lambda's
- reflection (code is data)
- dynamic (if it quacks like a duck)
- abstract away underlying system (for domain experts)
- backed by fast C++, C or Fortran libraries
column oriented data structure (APL influence)
0.2 Origins in LISP (demo)
myF <- function(x) { x+1 }
myF
function(x) { x+1 }
myF(1)
[1] 2
apply(matrix(c(1,2,3,4),2,2),1,myF)
[,1] [,2]
[1,] 2 3
[2,] 4 5
1. Why R
- AI coming out of winter (pit of dispair)
- Omnipresent in AI (Skill like SQL, Ecmascript)
- Open source, Eco-system, History (de facto standard)
- different backends (citizen scientist, Big Data, Keras backend)
- Backed by and integrated in Microsoft products
1.1 Notable R tools in ecosystem
- packages: https://cran.r-project.org/
- IDE Rstudio: https://www.rstudio.com/
- notable R packages:
1.2 Checklist: Is R the tool for me right now?
- Do I want to use free (like beer) software : yes
- Do I want to experiment with different models from different creators : yes
- Does my data fit in main memory of my computer (no big data ): yes
- Doesn't my data fit in main memory of my computer : yes with some prerequisites
2. R in Microsoft products
- 2015 Microsoft buys Revolution Analytics
- R Integrated in several products
- https://mran.microsoft.com
2.1 Microsoft R Open (R extension)
Improved some pittfals of typical R distribution (single threaded, standardization of packages and models, object orientation…) CRAN
(see Typescript)
2.2 Microsoft Machine Learning Server (run time)
Client/Server Operationalizing R
- DeployR
- ScaleR
Now also python
2.3 Microsoft R Archive Network (MRAN)
Standardized package, snapshotting in time https://mran.microsoft.com/
2.4 R in SQL Server
Machine learning services in SQL Server
2.5 R in visual studio
2.6 R in Power BI
Visualization with R? PowerBIR?? (jk :p)
2.7 Azure machine learning
2.8 Azure HDInsight (hadoop as a service)
2.9 Azure Databricks (spark as a service)
R one of the languages on Apache Spark (besides Scala and Python)
2.10 In minecraft
2.11 Certification as a Data Scientist
3. Concepts
In the following segment I try to provide a high level run through of basic concepts of AI, machine learning and statistics
3.1 AI
3.2 DIKW pyramid
3.3 DIKW cycle
3.4 Statistics and Machine learning
3.5 Error terms
In programming we try to reduce the errors in our models (programs) by fixing bugs and doing unit testing.
Reduce Errors Of Our statistical models : Stochastical element. Error is quantifiable with data.
3.5 Error terms
Model is estimation of errorterms of data around model
3.5 Error terms
Error has a probability distribution around: assumed to be normal distributed
3.6 Error types : hypothesis testing
3.6 Error types : hypothesis testing
- type I : incorrectly detect effect when there is none (bias, noise…) : overfitting
- type II : incorrectly detect no effect (0 hypothesis) when there is an effect : underfitting
Programming = mathematical proof testen (https://en.wikipedia.org/wiki/Curry%E2%80%93Howard_correspondence)
3.7 Information = Variance
we train our model and test/validate it on the same data
3.7 Information = Variance
How related is our training, test our validation data subset? Idempotency of our model.
3.8 Big data - Central Limit Theorem
Larger sample size: more normal distribution
with more data, we need less assumptions of underlying error term
3.9 Big data - Power increases with sample size
Power is chance that our model will correctly detect an effect. Power will decrease with more features in our model. Power will increase with larger sample size for our model.
3.10 Big data - Data is the new oil
Conclusion: the more (quality) data, the better for our models
New headaches: Horizontal scalability, distributed systems, CAP and CALM theoreum …
3.11 Deep learning
deep neural networks, long history, more data and faster hardware (GPU)
When in doubt use brute force ~ Ken Thompson
3.12 Deep learning
Renewed interest so some interesting developments
- Auto-encoder (encoder-decoder chained learned with internal representation)
- SEQ2SEQ (arbitrary size representation for e.g. NLP)
- Representation learning (less feature engineering)
- Theoretical framework (category theory): a new way of programming but with linear algebra http://colah.github.io/posts/2015-09-NN-Types-FP/
3.13 Deep dive theoretical
3.14 Level I in Causal hierarchy
3.15 Simpson paradox
4. R for the .NET programmer
In knitR it's also possible to run code from other languages:
- https://yihui.name/knitr/demo/engines/
- http://datadrivensecurity.info/blog/posts/2015/Jun/running-other-languages-in-r-markdown-files/
So let's add an engine for .net
eng_dotnet <- function(options) {
# create a temporary file
f <- basename(tempfile("dotnet", '.', paste('.', "dotnet", sep = '')))
on.exit(unlink(f)) # cleanup temp file on function exit
writeLines(options$code, f)
out <- ''
# if eval != FALSE compile/run the code, preserving output
if (options$eval) {
out <- system(sprintf('dotnet script %s', paste(f, options$engine.opts)), intern=TRUE)
}
# spit back stuff to the user
engine_output(options, options$code, out)
}
knitr::knit_engines$set(dotnet=eng_dotnet)
4.0 Let's try out our R-package
if (!requireNamespace("devtools", quietly = TRUE)) { install.packages("devtools") }
devtools::install_github("yenwel/Rpresdotnetengine", force=T)
library(Rpresdotnetengine)
knitr::knit_engines$set(dotnet=eng_dotnet)
4.1 Let's try out our new engine
var i = 1;
i++;
Console.WriteLine(i++);
Console.WriteLine(++i);
Console.WriteLine(i--);
Console.WriteLine(--i);
2
4
4
2
4.2 Basic types (.NET)
Console.WriteLine(1);
Console.WriteLine(true);
Console.WriteLine("hello world?");
enum Color {Red, Green, Blue};
Console.WriteLine(Color.Red);
1
True
hello world?
Red
4.2 Basic types (R)
1
[1] 1
2.0
[1] 2
T
[1] TRUE
'hello world?'
[1] "hello world?"
as.factor(c('Red','Green','Blue'))[1];
[1] Red
Levels: Blue Green Red
as.ordered(c('Best','Bester','Bestest'))[1];
[1] Best
Levels: Best < Bester < Bestest
4.3 Collections and composite types (.NET)
Console.WriteLine(new [] { 1 , 2 , 3}[1]);
Console.WriteLine(new List<object> { "Fred" , 20}[0]);
Console.WriteLine(new Dictionary<string,object>{{"name","Fred"},{"age",20}}["name"]);
Console.WriteLine(new { Name = "Fred", Age = 20});
2
Fred
Fred
{ Name = Fred, Age = 20 }
4.3 Collections and composite types (R)
c(1,2,3)
[1] 1 2 3
matrix(1:9, nrow=3,ncol=3)
[,1] [,2] [,3]
[1,] 1 4 7
[2,] 2 5 8
[3,] 3 6 9
list(name="Fred", age=20)
$name
[1] "Fred"
$age
[1] 20
# R has at least three ways to do OO (S3, S4, Reference class) but don't bother do FP rather
4.3 Collections and composite types (R)
array(1:16,dim = c(2,2,2,2))[,,2,2]
[,1] [,2]
[1,] 13 15
[2,] 14 16
data.frame(name = c("Buddy", "Lisa"), age = c(10, 38), sex = as.factor(c("m","f")))
name age sex
1 Buddy 10 m
2 Lisa 38 f
4.4 Functions (.NET)
Func<int,int> myF = (int x) => x + 1;
Console.WriteLine(myF);
Console.WriteLine(myF(1));
Console.WriteLine(new [] {1 , 2 , 3 , 4}.Select(myF).FirstOrDefault());
System.Func`2[System.Int32,System.Int32]
2
2
4.4 Functions (R)
myF <- function(x) { x+1 }
myF
function(x) { x+1 }
myF(1)
[1] 2
apply(matrix(c(1,2,3,4),2,2),1,myF)
[,1] [,2]
[1,] 2 3
[2,] 4 5
4.5 Deep dive into R
start here:
then google (CRAN because R is to confusing for google)
??something
5. Demo's
- supply chain analysis :
- shiny app connecting to database: https://github.com/yenwel/shinyDatabaseExplorer
- analyse load tests (connect to db): https://github.com/yenwel/analyse-neustar-loadtest
- process IIS Url Rewrite xml: https://github.com/yenwel/processsUrlRewrite
- this presentation: https://github.com/yenwel/R-presentation
- An R-package for the knitr dotnet engine: https://github.com/yenwel/Rpresdotnetengine
5.1 Shiny App
5.2.1 data mining bitcoin and twitter
#http://beautifuldata.net/2015/01/querying-the-bitcoin-blockchain-with-r/
library(Rbitcoin)
trades <- market.api.process('kraken',c('BTC','EUR'),'trades')
Rbitcoin.plot(trades, col='blue')
5.2.2 data mining bitcoin and twitter
## load rtweet package
library(rtweet)
# these environment variables are set via an app you can make at https://developer.twitter.com/en/apps
envvar <- Sys.getenv(c("TWT_R_APP", "TWT_R_API", "TWT_R_SECRET","TWT_R_XS_TOKEN","TWT_R_XS_SECRET"))
appname <- envvar[1]
key <- envvar[2]
secret <- envvar[3]
access_token <- envvar[4]
access_secret <- envvar[5]
## authenticate via access token
token <- create_token(
app = appname,
consumer_key = key,
consumer_secret = secret,
access_token = access_token,
access_secret = access_secret)
bitcoin_tweets <- search_tweets(q = "#bitcoin", n = 5000)
5.2.3 datamining bitcoin and twitter
str(trades)
List of 6
$ market : chr "kraken"
$ base : chr "BTC"
$ quote : chr "EUR"
$ timestamp : POSIXct[1:1], format: "2018-08-21 12:25:02"
$ market_timestamp: POSIXct[1:1], format: NA
$ trades :Classes 'data.table' and 'data.frame': 1000 obs. of 5 variables:
..$ date : POSIXct[1:1000], format: "2018-08-21 09:00:50" ...
..$ price : num [1:1000] 5617 5617 5617 5617 5616 ...
..$ amount: num [1:1000] 0.025 0.03 0.0642 0.00612 0.06811 ...
..$ tid : chr [1:1000] NA NA NA NA ...
..$ type : chr [1:1000] "bid" "bid" "ask" "ask" ...
..- attr(*, ".internal.selfref")=<externalptr>
5.2.3 datamining bitcoin and twitter
summary(trades)
Length Class Mode
market 1 -none- character
base 1 -none- character
quote 1 -none- character
timestamp 1 POSIXct numeric
market_timestamp 1 POSIXct numeric
trades 5 data.table list
5.2.3 datamining bitcoin and twitter
summary(trades$trades)
date price amount
Min. :2018-08-21 09:00:50 Min. :5562 Min. : 0.000000
1st Qu.:2018-08-21 09:22:40 1st Qu.:5583 1st Qu.: 0.008947
Median :2018-08-21 09:40:57 Median :5586 Median : 0.025449
Mean :2018-08-21 09:43:09 Mean :5589 Mean : 0.184183
3rd Qu.:2018-08-21 10:06:49 3rd Qu.:5594 3rd Qu.: 0.100000
Max. :2018-08-21 10:24:56 Max. :5617 Max. :10.227733
tid type
Length:1000 Length:1000
Class :character Class :character
Mode :character Mode :character
5.2.3 datamining bitcoin and twitter
str(bitcoin_tweets)
Classes 'tbl_df', 'tbl' and 'data.frame': 3940 obs. of 88 variables:
$ user_id : chr "985873110095335425" "985873110095335425" "985873110095335425" "985873110095335425" ...
$ status_id : chr "1031849591157743616" "1031845620812795906" "1031846050271768576" "1031848191619227649" ...
$ created_at : POSIXct, format: "2018-08-21 10:24:48" "2018-08-21 10:09:01" ...
$ screen_name : chr "CoinAirdropsAll" "CoinAirdropsAll" "CoinAirdropsAll" "CoinAirdropsAll" ...
$ text : chr "#Bitcoin #cryptocurrency #Airdrop\nNew Airdrop #ETHX <U+0001F4E2>\n\nEarn Up to 2500 #ETHX (~$500)\n\nETHX is a"| __truncated__ "#Bitcoin #Satoshi #Earn_Money #cryptocurrency #blockchain #Airdrop\nNew Airdrop #Kryptono <U+0001F4E2>\n\nLambo"| __truncated__ "#Bitcoin #Satoshi #cryptocurrency #blockchain #Airdrop\nNew Airdrop #NOIA <U+0001F4E2>\n\nEarn Up to 20000 #NOI"| __truncated__ "#Bitcoin #cryptocurrency #Airdrop\nNew Airdrop #CoinBundle <U+0001F4E2>\n\nEarn Up to 15,000 #BNDL (estimated v"| __truncated__ ...
$ source : chr "Twitter Lite" "Twitter Lite" "Twitter Lite" "Twitter Lite" ...
$ display_text_width : num 140 140 140 140 140 140 140 139 140 139 ...
$ reply_to_status_id : chr NA NA NA NA ...
$ reply_to_user_id : chr NA NA NA NA ...
$ reply_to_screen_name : chr NA NA NA NA ...
$ is_quote : logi FALSE FALSE FALSE FALSE FALSE FALSE ...
$ is_retweet : logi TRUE TRUE TRUE TRUE TRUE TRUE ...
$ favorite_count : int 0 0 0 0 0 0 0 0 0 0 ...
$ retweet_count : int 10 14 14 12 12 11 8 8 304 1467 ...
$ hashtags :List of 3940
..$ : chr "Bitcoin" "cryptocurrency" "Airdrop" "ETHX" ...
..$ : chr "Bitcoin" "Satoshi" "Earn_Money" "cryptocurrency" ...
..$ : chr "Bitcoin" "Satoshi" "cryptocurrency" "blockchain" ...
..$ : chr "Bitcoin" "cryptocurrency" "Airdrop" "CoinBundle" ...
..$ : chr "Bitcoin" "Crypto" "Satoshi" "Blockchain" ...
..$ : chr "Bitcoin" "cryptocurrency" "Airdrop" "Superbloom"
..$ : chr "Bitcoin" "Crypto" "Airdrop" "eByte" ...
..$ : chr "Bitcoin" "crypto" "STX" "ETHX"
..$ : chr NA
..$ : chr NA
..$ : chr NA
..$ : chr NA
..$ : chr "btc" "blockchain" "cryptocurrency" "crypto" ...
..$ : chr NA
..$ : chr "orbisico" "orbis" "cryptocurrency" "worldwide" ...
..$ : chr "MEMEZ"
..$ : chr NA
..$ : chr "Bitcoin"
..$ : chr "Bitcoin"
..$ : chr "bitcoin"
..$ : chr NA
..$ : chr "btc" "bitcoin"
..$ : chr "ETFs" "Bitcoin"
..$ : chr NA
..$ : chr NA
..$ : chr NA
..$ : chr "module" "ICO" "Bitcoin" "ETH" ...
..$ : chr "Crypto" "Cryptoforall"
..$ : chr NA
..$ : chr "Bitcoin" "cryptocurrency" "Airdrop" "Superbloom"
..$ : chr "Bitcoin" "Crypto" "Satoshi" "Blockchain" ...
..$ : chr "Bitcoin" "Satoshi" "cryptocurrency" "blockchain" ...
..$ : chr "Bitcoin" "Crypto" "STX" "ETHX"
..$ : chr "Bitcoin" "cryptocurrency" "Airdrop" "ETHX" ...
..$ : chr "Bitcoin" "Satoshi" "Earn_Money" "cryptocurrency" ...
..$ : chr "Bitcoin" "Crypto" "Airdrop" "eByte" ...
..$ : chr "Bitcoin" "cryptocurrency" "Airdrop" "CoinBundle" ...
..$ : chr "Bitcoin" "StableUSD"
..$ : chr NA
..$ : chr NA
..$ : chr "bitcoin"
..$ : chr "mine" "Bitcoin" "fast" "browser" ...
..$ : chr NA
..$ : chr NA
..$ : chr "Bitcoin"
..$ : chr "Bitcoin" "Satoshi" "cryptocurrency" "blockchain" ...
..$ : chr "bitcoin" "crypto" "STX" "ETHX"
..$ : chr "Bitcoin" "cryptocurrency" "Airdrop" "ETHX" ...
..$ : chr "Bitcoin" "cryptocurrency" "Airdrop" "Superbloom"
..$ : chr "Bitcoin" "cryptocurrency" "Airdrop" "CoinBundle" ...
..$ : chr "Bitcoin" "Satoshi" "Earn_Money" "cryptocurrency" ...
..$ : chr NA
..$ : chr "bitcoin" "traffic" "moneyonline"
..$ : chr "DEZOS" "DOS" "airdrop" "ico"
..$ : chr "ICO" "bitcoin" "btc" "bitcoinnews" ...
..$ : chr "ICO" "bitcoin" "btc" "bitcoinnews" ...
..$ : chr "ICO" "bitcoin" "btc" "bitcoinnews" ...
..$ : chr "ICO" "bitcoin" "btc" "bitcoinnews" ...
..$ : chr "ICO" "bitcoin" "btc" "bitcoinnews" ...
..$ : chr "China" "Pedowood" "WalkAway" "NortHKorea" ...
..$ : chr "module"
..$ : chr "Ransomware" "cybercrime" "bitcoin" "radionacionalderadio" ...
..$ : chr "bitcoin" "traffic" "moneyonline"
..$ : chr "BitcoinMovement" "mcg" "bitcoin" "cryptocurrency" ...
..$ : chr "bitcoinmovement" "bitcoin" "cryptocurrency" "mcg"
..$ : chr "BitexGlobal" "TokenSale" "ICO" "bitcoin" ...
..$ : chr "BitexGlobal" "TokenSale" "ICO" "bitcoin" ...
..$ : chr "BitexGlobal" "TokenSale" "ICO" "bitcoin" ...
..$ : chr "BitexGlobal" "TokenSale" "ICO" "bitcoin" ...
..$ : chr "ico" "blockchain" "bitcoin" "ethereum" ...
..$ : chr "Rubius" "ICO" "FinTech" "Blockchain"
..$ : chr "CapitalTechnologiesResearch" "blockchain" "privacy" "security" ...
..$ : chr "regulated" "Bitcoin"
..$ : chr "bitcoin" "Bitcointalk" "investors" "cryptocurrency"
..$ : chr "Crypto" "cryptocurrency" "PremierLeague" "football" ...
..$ : chr "ICO" "cryptocurrency" "bitcoin" "token"
..$ : chr "Bitcoin"
..$ : chr NA
..$ : chr "Bitcoin" "news"
..$ : chr "Bitcoin" "news"
..$ : chr "Bitcoin" "Bitcoin" "news"
..$ : chr "Bitcoin" "news"
..$ : chr NA
..$ : chr "airdrop" "ico" "btc" "BitCoin" ...
..$ : chr NA
..$ : chr NA
..$ : chr "ripple" "cryptocurrency" "coinbase" "bitcoin" ...
..$ : chr "ripple" "cryptocurrency" "coinbase" "bitcoin" ...
..$ : chr "ripple" "cryptocurrency" "coinbase" "bitcoin" ...
..$ : chr NA
..$ : chr NA
..$ : chr "ICODESICO" "DESICO" "DESI" "Crypto" ...
..$ : chr "bitcoin" "airdrop" "3cgDrive" "followers"
..$ : chr "bitcoin"
..$ : chr "ETFs" "Bitcoin"
..$ : chr "Bitcoin"
..$ : chr "Forex" "Stocks"
..$ : chr "Bristol" "monarchtoken" "cryptocurrency"
..$ : chr NA
.. [list output truncated]
$ symbols :List of 3940
..$ : chr NA
..$ : chr NA
..$ : chr NA
..$ : chr NA
..$ : chr NA
..$ : chr NA
..$ : chr NA
..$ : chr NA
..$ : chr NA
..$ : chr NA
..$ : chr NA
..$ : chr NA
..$ : chr NA
..$ : chr NA
..$ : chr NA
..$ : chr NA
..$ : chr NA
..$ : chr NA
..$ : chr NA
..$ : chr NA
..$ : chr NA
..$ : chr "BTC"
..$ : chr NA
..$ : chr NA
..$ : chr NA
..$ : chr NA
..$ : chr NA
..$ : chr NA
..$ : chr NA
..$ : chr NA
..$ : chr NA
..$ : chr NA
..$ : chr NA
..$ : chr NA
..$ : chr NA
..$ : chr NA
..$ : chr NA
..$ : chr NA
..$ : chr NA
..$ : chr NA
..$ : chr NA
..$ : chr NA
..$ : chr NA
..$ : chr NA
..$ : chr NA
..$ : chr NA
..$ : chr NA
..$ : chr NA
..$ : chr NA
..$ : chr NA
..$ : chr NA
..$ : chr "SPX" "DJI" "VIX" "AUDNZD" ...
..$ : chr NA
..$ : chr NA
..$ : chr NA
..$ : chr NA
..$ : chr NA
..$ : chr NA
..$ : chr NA
..$ : chr NA
..$ : chr NA
..$ : chr NA
..$ : chr NA
..$ : chr NA
..$ : chr NA
..$ : chr NA
..$ : chr NA
..$ : chr NA
..$ : chr NA
..$ : chr "btc" "eth"
..$ : chr NA
..$ : chr NA
..$ : chr NA
..$ : chr NA
..$ : chr NA
..$ : chr NA
..$ : chr NA
..$ : chr NA
..$ : chr NA
..$ : chr NA
..$ : chr NA
..$ : chr NA
..$ : chr NA
..$ : chr NA
..$ : chr NA
..$ : chr NA
..$ : chr NA
..$ : chr NA
..$ : chr NA
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..$ : chr NA
..$ : chr NA
..$ : chr NA
..$ : chr NA
..$ : chr NA
..$ : chr NA
..$ : chr NA
.. [list output truncated]
$ urls_url :List of 3940
..$ : chr NA
..$ : chr NA
..$ : chr NA
..$ : chr NA
..$ : chr NA
..$ : chr NA
..$ : chr NA
..$ : chr "goo.gl/di6Zzw"
..$ : chr NA
..$ : chr NA
..$ : chr NA
..$ : chr NA
..$ : chr NA
..$ : chr NA
..$ : chr "orbistransfer.com"
..$ : chr NA
..$ : chr NA
..$ : chr NA
..$ : chr NA
..$ : chr NA
..$ : chr NA
..$ : chr NA
..$ : chr NA
..$ : chr NA
..$ : chr NA
..$ : chr NA
..$ : chr "bitopro.com/token_exchange…"
..$ : chr NA
..$ : chr NA
..$ : chr NA
..$ : chr NA
..$ : chr NA
..$ : chr "goo.gl/di6Zzw"
..$ : chr NA
..$ : chr NA
..$ : chr NA
..$ : chr NA
..$ : chr "bbc.com/news/business-…"
..$ : chr NA
..$ : chr NA
..$ : chr "cryptonews.com.au/story/venezuel…"
..$ : chr "getcryptotab.com/2443681"
..$ : chr NA
..$ : chr NA
..$ : chr NA
..$ : chr NA
..$ : chr "goo.gl/di6Zzw"
..$ : chr NA
..$ : chr NA
..$ : chr NA
..$ : chr NA
..$ : chr "youtu.be/ZDE9eTOpwSo"
..$ : chr "bit.ly/2zSuzPr"
..$ : chr "bit.ly/2Mix9rH"
..$ : chr "bit.ly/2LcRg5h" "bit.ly/2GFT1Gp"
..$ : chr "bit.ly/2BvIkIJ" "bit.ly/2GFT1Gp"
..$ : chr "bit.ly/2BvG9op" "bit.ly/2GFT1Gp"
..$ : chr "bit.ly/2BtiOUe" "bit.ly/2GFT1Gp"
..$ : chr "bit.ly/2Lc0wGJ" "bit.ly/2GFT1Gp"
..$ : chr "youtu.be/OnJ4fTVDD2I"
..$ : chr NA
..$ : chr "goo.gl/5gNnmD"
..$ : chr "bit.ly/2zSuzPr"
..$ : chr "bit.ly/2LuX0bm"
..$ : chr "bit.ly/2ndYLiP"
..$ : chr NA
..$ : chr NA
..$ : chr NA
..$ : chr NA
..$ : chr "entrepreneur.com/article/318146"
..$ : chr "bit.ly/2nAEbJt"
..$ : chr NA
..$ : chr "coingeek.com/first-regulate…"
..$ : chr "buff.ly/2nPjHgi"
..$ : chr "buff.ly/2MDQgvU"
..$ : chr "netcurrencyindex.com"
..$ : chr NA
..$ : chr NA
..$ : chr "goo.gl/fb/2mhG9e"
..$ : chr "goo.gl/fb/jxJEaZ"
..$ : chr "goo.gl/fb/Q91p6J"
..$ : chr "goo.gl/fb/2Kmuuc"
..$ : chr NA
..$ : chr "bit.ly/2PpRGbC"
..$ : chr NA
..$ : chr NA
..$ : chr "bit.ly/2N4eqN0"
..$ : chr "bit.ly/2OXACZR"
..$ : chr "bit.ly/2OPrEO7"
..$ : chr "socialremit.com"
..$ : chr NA
..$ : chr "Desico.io" "twitter.com/desico_io/stat…"
..$ : chr NA
..$ : chr "btcmanager.com/coinbase-files…"
..$ : chr NA
..$ : chr NA
..$ : chr "copytrader.co"
..$ : chr "espn.com/jayski/cup/201…"
..$ : chr NA
.. [list output truncated]
$ urls_t.co :List of 3940
..$ : chr NA
..$ : chr NA
..$ : chr NA
..$ : chr NA
..$ : chr NA
..$ : chr NA
..$ : chr NA
..$ : chr "https://t.co/LRYBcPStxj"
..$ : chr NA
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..$ : chr NA
..$ : chr NA
..$ : chr NA
..$ : chr "https://t.co/Zc1vGTzjcR"
..$ : chr NA
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$ profile_banner_url : chr "https://pbs.twimg.com/profile_banners/985873110095335425/1523887582" "https://pbs.twimg.com/profile_banners/985873110095335425/1523887582" "https://pbs.twimg.com/profile_banners/985873110095335425/1523887582" "https://pbs.twimg.com/profile_banners/985873110095335425/1523887582" ...
$ profile_background_url : chr NA NA NA NA ...
$ profile_image_url : chr "http://pbs.twimg.com/profile_images/985883304669929472/BHGyfgvH_normal.jpg" "http://pbs.twimg.com/profile_images/985883304669929472/BHGyfgvH_normal.jpg" "http://pbs.twimg.com/profile_images/985883304669929472/BHGyfgvH_normal.jpg" "http://pbs.twimg.com/profile_images/985883304669929472/BHGyfgvH_normal.jpg" ...
5.2.3 datamining bitcoin and twitter
summary(bitcoin_tweets)
user_id status_id created_at
Length:3940 Length:3940 Min. :2018-08-21 07:57:08
Class :character Class :character 1st Qu.:2018-08-21 08:31:33
Mode :character Mode :character Median :2018-08-21 09:11:25
Mean :2018-08-21 09:10:56
3rd Qu.:2018-08-21 09:50:20
Max. :2018-08-21 10:24:48
screen_name text source
Length:3940 Length:3940 Length:3940
Class :character Class :character Class :character
Mode :character Mode :character Mode :character
display_text_width reply_to_status_id reply_to_user_id
Min. : 13 Length:3940 Length:3940
1st Qu.:139 Class :character Class :character
Median :140 Mode :character Mode :character
Mean :147
3rd Qu.:140
Max. :288
reply_to_screen_name is_quote is_retweet favorite_count
Length:3940 Mode :logical Mode :logical Min. : 0.0000
Class :character FALSE:3811 FALSE:1455 1st Qu.: 0.0000
Mode :character TRUE :129 TRUE :2485 Median : 0.0000
Mean : 0.3272
3rd Qu.: 0.0000
Max. :57.0000
retweet_count hashtags.Length hashtags.Class hashtags.Mode
Min. : 0.0 6 -none- character
1st Qu.: 0.0 7 -none- character
Median : 7.0 7 -none- character
Mean : 296.9 5 -none- character
3rd Qu.: 134.2 7 -none- character
Max. :10865.0 4 -none- character
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status_url name location
Length:3940 Length:3940 Length:3940
Class :character Class :character Class :character
Mode :character Mode :character Mode :character
description url protected followers_count
Length:3940 Length:3940 Mode :logical Min. : 0
Class :character Class :character FALSE:3940 1st Qu.: 131
Mode :character Mode :character Median : 795
Mean : 5781
3rd Qu.: 3705
Max. :1894456
friends_count listed_count statuses_count favourites_count
Min. : 0 Min. : 0.0 Min. : 1 Min. : 0.0
1st Qu.: 179 1st Qu.: 0.0 1st Qu.: 497 1st Qu.: 70.0
Median : 651 Median : 4.0 Median : 2278 Median : 621.5
Mean : 3842 Mean : 162.7 Mean : 23001 Mean : 6653.5
3rd Qu.: 3178 3rd Qu.: 25.0 3rd Qu.: 9926 3rd Qu.: 3116.0
Max. :482112 Max. :18058.0 Max. :1460197 Max. :683236.0
account_created_at verified profile_url
Min. :2006-11-18 06:51:03 Mode :logical Length:3940
1st Qu.:2014-03-15 16:23:25 FALSE:3926 Class :character
Median :2017-07-02 11:14:38 TRUE :14 Mode :character
Mean :2015-12-09 11:43:20
3rd Qu.:2018-02-09 15:35:06
Max. :2018-08-21 10:11:31
profile_expanded_url account_lang profile_banner_url
Length:3940 Length:3940 Length:3940
Class :character Class :character Class :character
Mode :character Mode :character Mode :character
profile_background_url profile_image_url
Length:3940 Length:3940
Class :character Class :character
Mode :character Mode :character
[ reached getOption("max.print") -- omitted 2804 rows ]
5.2.3 datamining bitcoin and twitter
summary(bitcoin_tweets$created_at)
Min. 1st Qu. Median
"2018-08-21 07:57:08" "2018-08-21 08:31:33" "2018-08-21 09:11:25"
Mean 3rd Qu. Max.
"2018-08-21 09:10:56" "2018-08-21 09:50:20" "2018-08-21 10:24:48"
5.2.4 datamining bitcoin and twitter
#https://www.rdocumentation.org/packages/MSBVAR/versions/0.9-2/topics/granger.test
#https://davegiles.blogspot.com/2012/11/granger-causality-testing-in-r.html